Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound?Based Deep Learning

Abstract

Objectives: The aim of this study was to develop pre-procedural intravascular ultrasound (IVUS)?based models for predicting the occurrence of stent underexpansion. Background: Although post-stenting IVUS has been used to optimize percutaneous coronary intervention, there are no pre-procedural guidelines to estimate the degree of stent expansion and provide preemptive management before stent deployment. Methods: A total of 618 coronary lesions in 618 patients undergoing percutaneous coronary intervention were randomized into training and test sets in a 5:1 ratio. Following the coregistration of pre- and post-stenting IVUS images, the pre-procedural images and clinical information (stent diameter, length, and inflation pressure; balloon diameter; and maximal balloon pressure) were used to develop a regression model using a convolutional neural network to predict post-stenting stent area. To separate the frames with from those without the occurrence of underexpansion (stent area <5.5 mm